View-based Projections have been proposed to project 3D point clouds into multiple views where 2D operations are applied. This approach improves the run-time for 3D point cloud inputs. MV3D [1]} fused the Lidar front view and bird-eye view along with the RGB image to generate 3D boxes on KITTI [2]}. As a result of this fusion, it outperformed the fully 3D approaches in terms of both run-time and accuracy. SqueezeSeg [3]} proposed spherical projections for Lidar data to construct a 2D image using the intensity, range, x, y and z coordinates as input channels; SqueezeNet is employed as its backbone network with a conditional random field as a post-processing step. PointSeg [4]} extended SqueezeSeg model to emphasize on the efficiency in run-time and accuracy. All of these approaches achieved efficient inference time on LiDAR data maintaining good accuracy.
